SoroushVahidi/scidocs huggingface dataset:
Anthropometric and physiological predispositions for elite soccer.
(2000)

This review is focused on anthropometric and physiological characteristics of soccer players with a view to establishing their roles within talent detection, identification and development programmes. Top-class soccer players have to adapt to the physical demands of the game, which are multifactorial. Players may not need to have an extraordinary capacity within any of the areas of physical performance but must possess a reasonably high level within all areas. This explains why there are marked individual differences in anthropometric and physiological characteristics among top players. Various measurements have been used to evaluate specific aspects of the physical performance of both youth and adult soccer players. The positional role of a player is related to his or her physiological capacity. Thus, midfield players and full-backs have the highest maximal oxygen intakes ( > 60 ml x kg(-1) x min(-1)) and perform best in intermittent exercise tests. On the other hand, midfield players tend to have the lowest muscle strength. Although these distinctions are evident in adult and elite youth players, their existence must be interpreted circumspectly in talent identification and development programmes. A range of relevant anthropometric and physiological factors can be considered which are subject to strong genetic influences (e.g. stature and maximal oxygen intake) or are largely environmentally determined and susceptible to training effects. Consequently, fitness profiling can generate a useful database against which talented groups may be compared. No single method allows for a representative assessment of a player's physical capabilities for soccer. We conclude that anthropometric and physiological criteria do have a role as part of a holistic monitoring of talented young players.
Security advances and challenges in 4G wireless networks
(2010)

This paper presents a study of security advances and challenges associated with emergent 4G wireless technologies. The paper makes a number of contributions to the field. First, it studies the security standards evolution across different generations of wireless standards. Second, the security-related standards, architecture and design for the LTE and WiMAX technologies are analyzed. Third, security issues and vulnerabilities present in the above 4G standards are discussed. Finally, we point to potential areas for future vulnerabilities and evaluate areas in 4G security which warrant attention and future work by the research and advanced technology industry.
Force feedback based gripper control on a robotic arm
(2016)

Telemanipulation (master-slave operation) was developed in order to replace human being in hazardous environment, with using a slave device (e.g. robot). In this paper many kind of master devices are studied, for the precise control. Remote control needs much information about remote environment for good and safe human-machine interaction. Existing master devices with vision system or audio feedback lacks most of such information, therefor force feedback, called haptics become significant recently. In this paper we propose a new concept of force feedback. This system can overcome the bottlenecks of other feedback system in a user friendly way. Force sensor and laser distance sensor communicates the information from the gripper's status to the teleoperator by using force feedback module on a glove. Pneumatic pressure gives the operator distance information, while a Magnetorheological Fluid (MR-Fluid) based actuator presents the gripper's force. The experiment result shows the possibility of usage of such force feedback glove in combination with a robotic arm.
Learning hierarchical control structures for multiple tasks and changing environments
(1998)

While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a di cult task. The bene t of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques.
Learning object detection from a small number of examples: the importance of good features
(2004)

Face detection systems have recently achieved high detection rates and real-time performance. However, these methods usually rely on a huge training database (around 5,000 positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multi-viewface detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable state of the art performance using only a few hundred training examples. For profile view faces, local orientation histograms enable learning a system that seems to outperform the state of the art in real-time systems even with a small number of training examples.
Feature Selection Using Adaboost for Face Expression Recognition
(2005)

We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based classifier, the Support Vector Machines (SVM) and identify the advantages of using AdaBoost over SVM in this context. We use histograms of Gabor and Gaussian derivative responses as the appearance features. We apply our approach to the face expression recognition problem where local appearances play an important role. Finally, we show that though SVM performs equally well, AdaBoost feature selection provides a final hypothesis model that can easily be visualized and interpreted, which is lacking in the high dimensional support vectors of the SVM.
Training Support Vector Machines: an Application to Face Detection
(1997)

Detection (To appear in the Proceedings of CVPR'97, June 17-19, 1997, Puerto Rico.) Edgar Osunay? Robert Freund? Federico Girosiy yCenter for Biological and Computational Learning and ?Operations Research Center Massachusetts Institute of Technology Cambridge, MA, 02139, U.S.A. Abstract We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classi ers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.
Fast Pose Estimation with Parameter-Sensitive Hashing
(2003)

Example-basedmethodsareeffectivefor parameterestimationproblemswhentheunderlyingsystemis simpleor thedimensionalityof the input is low. For complex andhigh-dimensional problemssuch asposeestimation,thenumberof required examplesand the computationalcomplexity rapidly becmeprohibitivelyhigh. We introducea new algorithm that learnsa setof hashingfunctionsthat efficiently index examplesrelevant to a particular estimationtask. Our algorithm extendsa recentlydevelopedmethodfor locality-sensitivehashing, which findsapproximateneighborsin timesublinearin thenumber of examples.Thismethoddependscritically on thechoiceof hashfunctions;weshowhowto find thesetof hashfunctions thatare optimallyrelevantto a particular estimationproblem.Experimentsdemonstr atethat theresultingalgorithm,which wecall Parameter -SensitiveHashing, canrapidlyandaccuratelyestimatethearticulatedposeof humanfiguresfroma large databaseof exampleimages. 0Part of thiswork wasdonewhenG.S.andP.V. werewith MitsubishiElectricResearchLabs,Cambridge,MA.
The effects of morphology and fitness on catastrophic interference
(2018)

Catastrophic interference occurs when an agent improves in one training instance but becomes worse in other instances. Many methods intended to combat interference have been reported in the literature that modulate properties of a neural controller, such as synaptic plasticity or modularity. Here, we demonstrate that adjustments to the body of the agent, or the way its performance is measured, can also reduce catastrophic interference without requiring changes to the controller. Additionally, we introduce new metrics to quantify catastrophic interference. We do not show that our approach outperforms others on benchmark tests. Instead, by more precisely measuring interactions between morphology, fitness, and interference, we demonstrate that embodiment is an important aspect of this problem. Furthermore, considerations into morphology and fitness can combine with, rather than compete with, existing methods for combating catastrophic interference.
Review of Dercum’s disease and proposal of diagnostic criteria, diagnostic methods, classification and management
(2012)

UNLABELLED
DEFINITION AND CLINICAL PICTURE: We propose the minimal definition of Dercum's disease to be generalised overweight or obesity in combination with painful adipose tissue. The associated symptoms in Dercum's disease include fatty deposits, easy bruisability, sleep disturbances, impaired memory, depression, difficulty concentrating, anxiety, rapid heartbeat, shortness of breath, diabetes, bloating, constipation, fatigue, weakness and joint aches.


CLASSIFICATION
We suggest that Dercum's disease is classified into: I. Generalised diffuse form A form with diffusely widespread painful adipose tissue without clear lipomas, II. Generalised nodular form - a form with general pain in adipose tissue and intense pain in and around multiple lipomas, and III. Localised nodular form - a form with pain in and around multiple lipomas IV. Juxtaarticular form - a form with solitary deposits of excess fat for example at the medial aspect of the knee.


EPIDEMIOLOGY
Dercum's disease most commonly appears between the ages of 35 and 50 years and is five to thirty times more common in women than in men. The prevalence of Dercum's disease has not yet been exactly established.


AETIOLOGY
Proposed, but unconfirmed aetiologies include: nervous system dysfunction, mechanical pressure on nerves, adipose tissue dysfunction and trauma. DIAGNOSIS AND DIAGNOSTIC METHODS: Diagnosis is based on clinical criteria and should be made by systematic physical examination and thorough exclusion of differential diagnoses. Advisably, the diagnosis should be made by a physician with a broad experience of patients with painful conditions and knowledge of family medicine, internal medicine or pain management. The diagnosis should only be made when the differential diagnoses have been excluded.


DIFFERENTIAL DIAGNOSIS
Differential diagnoses include: fibromyalgia, lipoedema, panniculitis, endocrine disorders, primary psychiatric disorders, multiple symmetric lipomatosis, familial multiple lipomatosis, and adipose tissue tumours. GENETIC COUNSELLING: The majority of the cases of Dercum's disease occur sporadically. A to G mutation at position A8344 of mitochondrial DNA cannot be detected in patients with Dercum's disease. HLA (human leukocyte antigen) typing has not revealed any correlation between typical antigens and the presence of the condition. MANAGEMENT AND TREATMENT: The following treatments have lead to some pain reduction in patients with Dercum's disease: Liposuction, analgesics, lidocaine, methotrexate and infliximab, interferon α-2b, corticosteroids, calcium-channel modulators and rapid cycling hypobaric pressure. As none of the treatments have led to long lasting complete pain reduction and revolutionary results, we propose that Dercum's disease should be treated in multidisciplinary teams specialised in chronic pain.


PROGNOSIS
The pain in Dercum's disease seems to be relatively constant over time.
Mapping the game landscape: Locating genres using functional classification
(2009)

Are typical computer game genres still valid descriptors and useful for describing game structure and game content? Games have changed from simple to complex and from single function to multi function. By identifying structural differences in game elements we develop a more nuanced model to categorized games and use cluster analysis as a descriptive tool in order to do so. The cluster analysis of 75 functionally different games shows that the two perspectives (omnipresent and vagrant), as well as challenges, mutability and savability are important functional categories to use in order to describe games. Author
Reasoning for Semantic Error Detection in Text
(2017)

Identifying incorrect content (i.e., semantic error) in text is a difficult task because of the ambiguous nature of written natural language and themany factors that canmake a statement semantically erroneous. Current methods identify semantic errors in a sentence by determining whether it contradicts the domain to which the sentence belongs. However, because thesemethods are constructedon expected logic contradictions, they cannot handle new or unexpected semantic errors. In this paper, we propose a new method for detecting semantic errors that is based on logic reasoning. Our proposed method converts text into logic clauses, which are later analyzed against a domain ontology by an automatic reasoner to determine its consistency. This approach can provide a complete analysis of the text, since it can analyze a single sentence or sets of multiple sentences. When there are multiple sentences to analyze, in order to avoid the high complexity of reasoning over a large set of logic clauses, we propose rules that reduce the set of sentences to analyze, based on the logic relationships between sentences. In our evaluation, we have found that our proposed method can identify a significant percentage of semantic errors and, in the case of multiple sentences, it does so without significant computational cost. We have also found that both the quality of the information extraction output and modeling elements B Dejing Dou dou@cs.uoregon.edu Fernando Gutierrez fernando@cs.uoregon.edu Nisansa de Silva nisansa@cs.uoregon.edu Stephen Fickas fickas@cs.uoregon.edu 1 Department of Computer and Information Science, 1202 University of Oregon, Eugene, OR 97403, USA of the ontology (i.e., property domain and range) affect the capability of detecting errors.
Panda: Public Auditing for Shared Data with Efficient User Revocation in the Cloud
(2013)

With data storage and sharing services in the cloud, users can easily modify and share data as a group. To ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Moreover, our mechanism is able to support batch auditing by verifying multiple auditing tasks simultaneously. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.
100kHz IGBT inverter use of LCL topology for high power induction heating
(2011)

A power electronic inverter is developed for a high frequency induction heating application. This equipment is available for the implementation of ZVS LCL resonance method was used. Using IGBT 20MVA, 100kHz LCL resonant induction heating equipment during the development process, 100kHz IGBT module development process and understanding gained from the design of LCL resonant Zero Crossing through the PLL control over operation of the IGBT Soft Switching, and focused on minimizing losses. In addition, through the actual design and implementation of devices using the area to test drive the development of large inverter technology based on the test
P-MOD: Secure Privilege-Based Multilevel Organizational Data-Sharing in Cloud Computing
(2018)

Cloud computing has changed the way enterprises store, access and share data. Data is constantly being uploaded to the cloud and shared within an organization built on a hierarchy of many different individuals that are given certain data access privileges. With more data storage needs turning over to the cloud, finding a secure and efficient data access structure has become a major research issue. With different access privileges, individuals with more privileges (at higher levels of the hierarchy) are granted access to more sensitive data than those with fewer privileges (at lower levels of the hierarchy). In this paper, a Privilege-based Multilevel Organizational Data-sharing scheme (P-MOD) is proposed that incorporates a privilege-based access structure into an attribute-based encryption mechanism to handle these concerns. Each level of the privilege-based access structure is affiliated with an access policy that is uniquely defined by specific attributes. Data is then encrypted under each access policy at every level to grant access to specific data users based on their data access privileges. An individual ranked at a certain level can decrypt the ciphertext (at that specific level) if and only if that individual owns a correct set of attributes that can satisfy the access policy of that level. The user may also decrypt the ciphertexts at the lower levels with respect to the user’s level. Security analysis shows that P-MOD is secure against adaptively chosen plaintext attack assuming the DBDH assumption holds. The comprehensive performance analysis demonstrates that PMOD is more efficient in computational complexity and storage space than the existing schemes in secure data sharing within an organization.
Generating ROC curves for artificial neural networks
(1994)

Receiver operating characteristic (ROC) analysis is an established method of measuring diagnostic performance in medical imaging studies. Traditionally, artificial neural networks (ANN's) have been applied as a classifier to find one "best" detection rate. Recently researchers have begun to report ROC curve results for ANN classifiers. The current standard method of generating ROC curves for an ANN is to vary the output node threshold for classification. Here, the authors propose a different technique for generating ROC curves for a two class ANN classifier. They show that this new technique generates better ROC curves in the sense of having greater area under the ROC curve (AUC), and in the sense of being composed of a better distribution of operating points.
Socio-emotional development: from infancy to young adulthood.
(2009)

Results from the Uppsala Longitudinal Study (ULS), which started in 1985, are reported in two sections. The first section gives a summary of longitudinal data from infancy to middle childhood (age 9 years; n = 96) concerning predictions of social functioning aspects from the theoretical perspectives of temperament, attachment, and health psychology (social factors). The second section presents the first results emanating from a follow-up when participants were 21 years old (n = 85). The developmental roots of social anxiety symptoms were studied from the same perspectives as above, although with a special focus on the predictive power of the temperament trait of shyness/inhibition. Results for middle childhood outcomes showed that temperament characteristics were relevant for most outcomes, whereas the contribution of attachment was most convincingly shown in relation to social competence and personality. Social factors were found to have moderating functions, but direct effects were also shown, the most interesting perhaps being positive effects of non-parental day care. Results from the 21-year data confirmed the expected predictive relation from shyness/inhibition to symptoms of social anxiety and further showed this relation to be specific; the relation to symptoms of depression did not survive control for social anxiety, although the opposite was true. The broad analysis of predictor associations with social anxiety, showing the relevance of other temperament factors as well as interactive effects, again attested to the need for multi-faceted models to analyze developmental trajectories.
Contingency planning over probabilistic hybrid obstacle predictions for autonomous road vehicles
(2010)

This paper presents a novel optimization based path planner that can simultaneously plan multiple contingency paths to account for the uncertain actions of dynamic obstacles. This planner addresses the particular problem of collision avoidance for autonomous road vehicles which are required to safely interact with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline based trajectory representation and fast but accurate collision probability approximations to enable the simultaneous optimization of multiple contingency paths.